How To Become a DevOps Engineer: A Comprehensive Guide

If you’re at all familiar with the software development process, then you’ll know that there are a lot of moving parts. It’s usually the case that you aren’t just collaborating with members of your own team. To build lasting software and applications, your team will also need to coordinate and collaborate with other teams too. 

There’s an entire field dedicated to enhancing and optimizing workflows across all these teams, and it’s called DevOps. DevOps engineers bridge workflows with automated processes, accelerated timelines, and more accountability across various teams, including developers, data engineers, and software engineers.

DevOps is an approach to software development that emerged in 2007, and since then, it’s become quite a lucrative subset of software development, with entry-level salaries starting in the six figures. But given that DevOps is a relatively new field, the path to becoming a DevOps engineer isn’t always clear. 

That’s why we’ve assembled this guide. Below, we’ll outline ten steps that you can take to launch a career in DevOps today.

What Does a DevOps Engineer Do?

DevOps engineers oversee infrastructure and software workflows for an organization. They build automation frameworks, optimize pipelines for apps, write software code, and mitigate issues during software releases. In other words, DevOps engineers bridge siloed teams to build better software. 

Is It Hard To Become a DevOps Engineer?

It’s not hard to become a DevOps engineer, but you will need a tailored learning plan. DevOps often doesn’t entail fixed responsibilities. It combines a variety of practices and philosophies, so it’s essential that you understand those before you start your learning journey.

How To Become a DevOps Engineer: A 10-Step Guide

Follow these 10 steps to become a DevOps engineer:

Learn the Fundamentals

Before you jump into a degree or bootcamp, dip your toes into the fundamentals. At the very beginning of your learning journey, your goal should be to have a rudimentary understanding of the following:

• What DevOps entails
• Continuous Integration and Continuous Delivery (CI/CD)
• Infrastructure as a Service (IaaS) v. Platform as a Service (PaaS)
• Containers
• Networking
• Virtualization
• Provisioning

Learn Coding and Scripting

The “Dev” in DevOps is all about developing infrastructure code. Python, JavaScript, and Ruby are a must for a DevOps engineer. Even though you won’t be building new software, you need to know how to code, as DevOps Engineers are responsible for managing updates and code releases.

Meet the Educational Requirements (and Continue Learning)

Here are the two educational paths you can take for a career as a DevOps engineer:

University Degree

A college degree is not necessary to become a DevOps engineer, but if you choose the traditional path of a university degree, you can pursue a bachelor’s degree in computer science, computer engineering, software engineering, or any IT field.

Bootcamp

Instead of spending 3-4 years getting a degree, you can learn everything you need for your first DevOps job with a bootcamp. You will spend less time and a fraction of the money and have a more nuanced learning experience than at a traditional college. Springboard’s 9-month Software Engineering Bootcamp is great for aspiring DevOps engineers, and the program offers a money-back guarantee if you don’t land a job soon after graduation.

Continuous Learning

Regardless of your chosen path, you must keep your knowledge and skills up to date, as the technologies you’ll use will keep changing. Keep up with the following resources:

Online Courses

Check out these DevOps courses that can help keep you relevant:

• Udemy’s DevOps Masterclass, which covers Terraform, Jenkins, Kubernetes, and Ansible
• IBM’s Introduction to DevOps, which covers core concepts like CI/CD, infrastructure as code, behavior-driven development, microservices, etc.
• Springboard’s coding bootcamp for the essential programming and scripting languages
• Udemy’s Docker Bootcamp

Blogs and Books

Old-school professionals out there can add these books to their reading list:

• The Phoenix Project and the Unicorn Project by Gene Kim, the CTO of Tripwire, and Kevin Behr, the founder of the Information Technology Institute
• The DevOps Handbook, also authored by Gene Kim, along with other IT scholars and leaders
• Google’s Site Reliability Engineering guides, which contains three books on designing and managing systems
• DevOps for Dummies, by Emily Freeman

You can also bookmark these blogs for frequent updates:

• The Microsoft Azure Blog for Azure-related information and cloud engineering updates
• The Agile Admin, which also contains helpful YouTube videos
• The Puppet Blog, with case studies, reports, and webinars, as well

More of a podcaster? Give Arrested DevOps a try.

Follow DevOps Specialists on Social Media

Many DevOps specialists use social media to spread their knowledge, resources, and tips to budding DevOps engineers. Here are some DevOps specialists to check out:

• Nana Janashia runs a YouTube channel, conducts bootcamps, and shares valuable resources on her LinkedIn.
• Gene Kim doesn’t post content directly on LinkedIn but shares links to his talks, seminars, and the latest conferences in the DevOps field. His Twitter has some interesting posts too.
• Dr. Nicole Forsgren (Microsoft and ex-GitHub) shares employment opportunities and funny but topical posts related to DevOps on her Twitter feed.

Hone Your Skillset

To launch a career as a DevOps engineer, you’ll need the following mix of technical and soft skills:

Technical Skills

• Building incident management processes. DevOps engineers need to identify system weaknesses and devise proactive solutions. These include logging and categorizing, notifying and escalating, and eliminating issues.
• Optimizing delivery pipelines. DevOps pipelines are a bundle of automated workflows and tools that streamline the work of software developers and other IT professionals. A DevOps engineer must be able to build and optimize delivery pipelines and know how to choose the right DevOps tools.
• Technical writing. Writing playbooks and post-mortem reports involves a deep understanding of technical vocabulary. It’s vital that you be able to expand upon each step and the tools that were used, with the appropriate terminology.

Soft Skills

• Communication and problem-solving. A DevOps engineer’s main job is coordinating with other IT teams to ensure smooth workflow across operations, development, and management teams. This requires bringing everyone on the same page through effective communication skills and putting out fires with problem-solving.
• Self-education. The DevOps space is constantly evolving, so you’ll need to upskill continually. Bootcamps and YouTube tutorials are a great way to do this.
• Collaboration. DevOps is a collaborative process, so you’ll need to be adept at working with other teams to improve code, conduct peer reviews for codes, and streamline workloads.

Learn the Tools

If you’re a DevOps engineer, then you’ll need to understand how to use Linux—the entire industry is practically built upon this operating system. You’ll also use Git, a popular control system, for version control and source code management. DevOps engineers ensure collaboration among software engineers and developers, and Git helps with that.

Jenkins is another must-have tool, as it’s used to manage CI/CD pipelines. It’s easy to learn, and with its numerous plugins, you can use it and many other tools and deployment processes.

Another essential tool is Docker, which is like a container for code. You can build and deploy applications on Docker and release them to multiple servers. Docker stores your entire software stack(s) for you without mixing them up.

You’ll need to learn more tools eventually, but those three are a great place to get started. 

Gain Practical Experience

You can get practical experience before you land a job. Here’s how:

Sample Projects

One of the easiest software development projects to start with is to create a chatbot for customer support. If you want to work with cloud computing, you can learn how to migrate an application to the cloud. This would involve data migration and configuring resources using cloud technologies.

Participate in Hackathons

Hackathons are the best way to apply your knowledge while also building a network. It teaches you how to generate ideas and collaborate in fast-paced environments. You can check out websites like Devpost to find hackathons that interest you.

Pursue Volunteer Work

Organizations with large IT development teams require DevOps engineers to ensure the smooth deployment of software. Volunteer with businesses selling IT products and help them streamline their IT operations.

Pursue a Certification

Certifications validate your knowledge and expertise, and when you start applying for jobs, they’ll help you stand out from other candidates. These certifications can help with salary negotiations and advanced career opportunities:

• Amazon Web Services Certified DevOps Engineer certification
• Certified Kubernetes Administrator
• Microsoft Certified: DevOps Engineer Expert

Consider an Internship

Start looking for internships once you have a body of sample work that you can show employers. Look for companies that give placement offers based on internships. Apply your knowledge, make connections, and ask plenty of questions.

Your Network Is Your Net Worth

Building professional relationships can quickly get you that referral or job opportunity. It will also enhance your growth as you learn from (and along with) your network. Here’s how you build your network:

• LinkedIn. Share your experiences as someone entering the industry. Engage with seasoned DevOps engineers who share resources and advice.
• Online Communities. DevOps communities on Reddit, Slack, and Github are great for nurturing relationships as you navigate project-specific obstacles. Make sure to help those you can. Your experience as someone building unique learning paths is valuable as well.
• Conferences And Meetups. Community meetups and conferences are great for forging relationships the old-school way. You can learn about other people’s journeys without being limited to a character limit for posts online.

Polish Your Resume and Prepare for Interviews

Build a resume for each job position you are applying to so that you are highlighting exactly what each hiring manager needs.

Career Transition to DevOps Engineering: Where To Start

Your starting position will determine your career path as a DevOps Engineer. Here are two possibilities:

Related Career Transition

If you’re already a data engineer, a software engineer, or a cloud engineer, the transition to DevOps will be easy. Immerse yourself in DevOps teams that manage the pipelines familiar to you. That will be a sure-shot path to DevOps roles. You can then use the above guide to fill in knowledge gaps. There’s no need to start from scratch. You’ll be able to make this transition within months.

Unrelated Career Transition

If you’re making an unrelated career transition, then your path will be more akin to someone starting from scratch. But remember—you are coming from a position of some experience. Your training as part of any career has equipped you with a bunch of basic skills (including soft skills and leadership skills) that will be invaluable as a DevOps engineer.

How Much Can You Earn as a DevOps Engineer?

The average salaries of DevOps engineers fall in the 100K-165K range based on experience:

Junior DevOps Engineer

A junior DevOps engineer can easily make six figures:

Mid-Level DevOps Engineer

A mid-level DevOps Engineer, which entails about 7 years of experience, can expect to make closer to $150,000:

Senior DevOps Engineer

On the higher range of things, the annual salary for a Senior DevOps Engineer can reach $200,000.

What Does the Career Path of a DevOps Engineer Look Like?

A DevOps role encompasses many qualifications, so there are many ways to get there. Linux is one of the building blocks for DevOps, so starting out as a Linux engineer is a common starting point. Another career path could be to start as a release manager. You will manage the end-to-end software delivery life cycle and transition into project manager or IT director positions. You can also start as a technician specialist and work your way up from there.

Becoming a DevOps Engineer: Real-Life Examples To Learn From

Here are two examples of DevOps Engineers who have successfully carved their career paths in the industry:

Linh Luong

Linh worked in the retail industry for ten years before pivoting into tech. Linh prioritized the following to land her first DevOps job:

• Terraform
• CI/CD pipeline management
• Linux
• Git
• Python as the primary programming language
• Ansible, which is a configuration management tool

Linh became proficient in the above through free courses, blogs, and books. She advises professionals to experiment with different languages and relevant tools before committing to a few. 

Nana Janashia

Nana recommends that you start by learning to differentiate between DevOps concepts (the foundation of DevOps processes) and DevOps tools (which solve a problem within those processes). Understand the concepts first, and then see which tool can solve the challenges at hand.

Nana suggests going through video tutorials and official documentation for each tool. She highlights the importance of knowing how to do each step and understanding its reason rather than just having an overall idea.

FAQs About Becoming a DevOps Engineer

We’ve got the answers to your most frequently asked questions:

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!